Deep learning approach for active classification of electrocardiogram signals
In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint....
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| Vydané v: | Information sciences Ročník 345; s. 340 - 354 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Inc
01.06.2016
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| Predmet: | |
| ISSN: | 0020-0255, 1872-6291 |
| On-line prístup: | Získať plný text |
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| Abstract | In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods. |
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| AbstractList | In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods. |
| Author | Yager, R.R. Bazi, Yakoub Alajlan, Naif AlHichri, Haikel Melgani, Farid Rahhal, M.M. Al |
| Author_xml | – sequence: 1 givenname: M.M. Al surname: Rahhal fullname: Rahhal, M.M. Al organization: ALISR Laboratory, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh 11543, Saudi Arabia – sequence: 2 givenname: Yakoub orcidid: 0000-0001-9287-0596 surname: Bazi fullname: Bazi, Yakoub email: yakoub.bazi@gmail.com, ybazi@ksu.edu.sa organization: ALISR Laboratory, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh 11543, Saudi Arabia – sequence: 3 givenname: Haikel surname: AlHichri fullname: AlHichri, Haikel email: hhichri@ksu.edu.sa organization: ALISR Laboratory, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh 11543, Saudi Arabia – sequence: 4 givenname: Naif surname: Alajlan fullname: Alajlan, Naif email: najlan@ksu.edu.sa organization: ALISR Laboratory, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh 11543, Saudi Arabia – sequence: 5 givenname: Farid surname: Melgani fullname: Melgani, Farid email: melgani@disi.unitn.it organization: Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, I-38123 Trento, Italy – sequence: 6 givenname: R.R. surname: Yager fullname: Yager, R.R. email: yager@panix.com organization: Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, USA |
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| Title | Deep learning approach for active classification of electrocardiogram signals |
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